VBGS-SLAM uses variational inference on conjugate Gaussian properties to couple 3DGS map refinement and pose tracking with closed-form updates and posterior uncertainty, reducing drift compared to deterministic methods.
DynaGSLAM: Real-Time Gaussian-Splatting SLAM for Online Rendering, Tracking, and Motion Prediction
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Multi-level DWT frequency modulation in 3DGS reduces Gaussian counts by recursive low-frequency decomposition and a single scaling parameter while preserving rendering quality.
DynoSLAM embeds stochastic GNN-based pedestrian forecasts via Monte Carlo rollouts and a dynamic Mahalanobis factor into GraphSLAM to maintain accurate tracking and produce probabilistic safety envelopes in crowded scenes.
citing papers explorer
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VBGS-SLAM: Variational Bayesian Gaussian Splatting Simultaneous Localization and Mapping
VBGS-SLAM uses variational inference on conjugate Gaussian properties to couple 3DGS map refinement and pose tracking with closed-form updates and posterior uncertainty, reducing drift compared to deterministic methods.
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Learnable Multi-level Discrete Wavelet Transforms for 3D Gaussian Splatting Frequency Modulation
Multi-level DWT frequency modulation in 3DGS reduces Gaussian counts by recursive low-frequency decomposition and a single scaling parameter while preserving rendering quality.
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DynoSLAM: Dynamic SLAM with Generative Graph Neural Networks for Real-World Social Navigation
DynoSLAM embeds stochastic GNN-based pedestrian forecasts via Monte Carlo rollouts and a dynamic Mahalanobis factor into GraphSLAM to maintain accurate tracking and produce probabilistic safety envelopes in crowded scenes.